Quantitative Evaluation.   
We evaluate the depth maps using the standard metrics in [1]}, including mean absolute relative error (abs-rel), mean absolute error (abs), squared relative error (sq-rel), root mean square error in linear scale (rmse) and log scale (rmse-log), and inlier ratios under thresholds of \(\sigma <\) 1.25/1.25\(^2\) /1.25\(^3\) . Tab. REF  shows the results on the ScanNet benchmark of our methods and several state-of-the-art MVS methods. We compare three variants of our models: i) base: a base version with our recurrent indexing cost volume, ii) +pose: with residual pose net, and iii) +pose,atten: a full version with residual pose and the asymmetric employment of transformer self-attention. Our full model achieves the best performance in most metrics except two inlier ratios under \(\sigma <\) 1.25\(^2\)  and 1.25\(^3\) , outperformed by DELTAS [2]} and ESTD [3]}. But these two inlier ratios are quite loose metrics, not as essential as abs-rel and abs.
